Book Image

Java for Data Science

By : Richard M. Reese, Jennifer L. Reese
Book Image

Java for Data Science

By: Richard M. Reese, Jennifer L. Reese

Overview of this book

para 1: Get the lowdown on Java and explore big data analytics with Java for Data Science. Packed with examples and data science principles, this book uncovers the techniques & Java tools supporting data science and machine learning. Para 2: The stability and power of Java combines with key data science concepts for effective exploration of data. By working with Java APIs and techniques, this data science book allows you to build applications and use analysis techniques centred on machine learning. Para 3: Java for Data Science gives you the understanding you need to examine the techniques and Java tools supporting big data analytics. These Java-based approaches allow you to tackle data mining and statistical analysis in detail. Deep learning and Java data mining are also featured, so you can explore and analyse data effectively, and build intelligent applications using machine learning. para 4: What?s Inside ? Understand data science principles with Java support ? Discover machine learning and deep learning essentials ? Explore data science problems with Java-based solutions
Table of Contents (19 chapters)
Java for Data Science
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface

Additional network architectures and algorithms


We have discussed a few of the most common and practical neural networks. At this point, we would also like to consider some specialized neural networks and their application in various fields of study. These types of networks do not fit neatly into one particular category, but may still be of interest.

The k-Nearest Neighbors algorithm

An artificial neural network implementing the k-NN algorithm is similar to MLP networks, but it provides significant reduction in time compared to the winner takes all strategy. This type of network does not require a training algorithm after the initial weights are set and has fewer connections among its neurons. We have chosen not to provide an example of this algorithm's implementation because its use in Weka is very similar to the MLP example.

This type of network is best suited to classification tasks. Because it utilizes lazy learning techniques, reserving all computation until after information has been...